Our results show that published biomass allometric equations from regional and national sources can give substantial variation in plot-level biomass estimates, especially in denser plots. The varia-tion may suggest that large biomass plots are not well represented in the national scale allometric equations derivation due to the fact that it is more time consuming and labor intensive to harvest large biomass plots. In spite of the fact that the biomass density for two sets of allometric equations hold similar patterns statistically, the differences in the biomass density are obvious, indicating the importance of assessing the influence of varied allometric equa-tions in predicting biomass with small footprint lidar systems. Since both sets of allometric equations employ DBH as input, and the regional allometric equations use one more additional vari-able (i.e., height), the difference between biomass density at the plot level mainly reflects the aggregated variation of height for all individual trees in a plot. As a result, the comparison of AGB using those two sets of allometric equations shows that tree heights in our study area may generally be higher than the average heights of the same species group across the country. In models with reference above ground biomass calculated from regional biomass equations, the integration of a simplified, empir-ical relationship between DBH and height generally improved the performance of the regression models. Its utility can be explained by the fact lidar-derived variables are directly related to height, while reference AGBs calculated from either Jenkins allometric equations or regional allometric equations share a common input, DBH. The simplified transformation from height metrics to volu-metric metrics made the association between reference AGBs and